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UiPath UiPath-AAAv1 Exam Syllabus Topics:

Section	Objectives
Prompt Engineering	- Prompt Design Techniques 1. Structured prompting 2. LLM interaction best practices 3. Prompt optimization
Agentic Discovery	- Identifying Automation Opportunities 1. Business process evaluation 2. Use case discovery 3. Agent suitability assessment
Context Grounding and Escalations	- Enterprise-Ready Agent Design 1. Data source integration 2. Human-in-the-loop escalation 3. Context grounding
Agentic Evaluations	- Evaluation and Optimization 1. Performance measurement 2. Reliability and governance 3. Evaluation strategies
Agentic AI and Automation Concepts	- Foundations of Agentic Automation 1. Human, robot, and agent collaboration 2. AI and automation fundamentals 3. UiPath Agentic Automation ecosystem
Agent Blueprint Design	- Designing Intelligent Agents 1. Goal and workflow definition 2. Prompt and instruction design 3. Agent orchestration concepts

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UiPath Certified Professional Agentic Automation Associate (UiAAA) Sample Questions (Q44-Q49):

NEW QUESTION # 44
What configuration options are available for setting up Context Grounding in UiPath?

A. Configuration is limited to enabling Context Grounding without any integration with Orchestrator or folder permissions.
B. You can configure Context Grounding by creating indexes in Orchestrator, managing folder-level permissions, selecting an LLM from the LLM Gateway, and syncing data using the Update Context Grounding Index activity.
C. Context Grounding requires default settings without any options for index creation or LLM selection.
D. Context Grounding setup relies entirely on manual indexing and lacks automated sync capabilities.

Answer: B

Explanation:
Bis correct - UiPath providesend-to-end configuration capabilitiesforContext Grounding, including:
* Creating indexesin Orchestrator
* Controlling accessviafolder-level permissions
* Selecting LLMsfrom theLLM Gateway
* Keeping indexesup to dateusing theUpdate Context Grounding Index activity This allows agents to accessreal-time enterprise context, reducing hallucinations and enhancing accuracy when performing actions or generating responses.
Option A underestimates the feature scope.
C and D are incorrect - UiPath supportsautomated syncs, granular access control, andmulti-model compatibility.
UiPath's platform treats grounding as agoverned, scalable enterprise feature, critical for AI safety and relevance.

NEW QUESTION # 45
When would it be most appropriate to use Web Search instead of Web Reader in an agent workflow?

A. When detailed, structured data is required from a known supplier's webpage.
B. When the user needs a summarized overview from multiple public sources without a specific URL.
C. When extracting time-sensitive data from a secure internal system.
D. When accessing and filtering information already embedded within a private enterprise knowledge base.

Answer: B

Explanation:
Cis correct - useWeb Searchin an agent workflow when you need the LLM toquery public internet sources(e.g., news, pricing, documentation), butdon't have a specific URL.
UiPath Autopilot and Agentic Agents distinguish:
* Web Search: For open-ended discovery from the web (e.g., "find latest refund policies from airlines")
* Web Reader: For extracting or summarizing content from aspecific, known URLor internal portal Web Search is ideal for:
* Aggregating public info
* Real-time summaries
* Context retrieval for grounding the prompt
A and B involveinternal sources- use tools likeKnowledge RetrievalorAPI connectorsinstead.
D calls fortargeted extraction, better suited toWeb Readerwith structured parsing.

NEW QUESTION # 46
Four draft system prompts are shown for an invoice-approval agent. Based on UiPath guidance for context, instruments, and output format constraints, which draft is the most robust choice?

A. You are an invoice-approval agent who deals only with supplier invoices and rejects any other request.
Extract invoice_ID from the email text.
When an invoice_ID is found, run the LookupInvoice tool to retrieve invoice amount and supplier name.
If the total # $10,000, escalate the case to Finance in Action Center, sending invoice_ID, amount, and supplier.
If the total > $10,000, approve the invoice.
Return a reply wrapped inside invoice_status: tags: use <approved> or <awaiting_review> as appropriate.
Follow a concise, professional tone and refuse tasks outside invoice approval.
B. You are an invoice approver. After processing, output exactly the following JSON template:
{ "id": "ABC-123", "status": "approved", "amount": 9999.9 }
Extract {{invoice_ID}} from the email text.
When an {{invoice_ID}} is found, run the LookupInvoice tool to retrieve invoice amount and supplier name.
If the total # $10,000, escalate the case to Finance in Action Center, sending {{invoice_ID}}, amount, and supplier.
If the total > $10,000, approve the invoice.
Populate the fields above with real data.
C. You are an invoice-approval agent who deals only with supplier invoices and rejects any other request.
Extract {{invoice_ID}} from the email text.
When an {{invoice_ID}} is found, run the LookupInvoice tool to retrieve invoice amount and supplier name.
If the total # $10,000, escalate the case to Finance in Action Center, sending {{invoice_ID}}, amount, and supplier.
If the total > $10,000, approve the invoice.
Return a reply wrapped inside invoice_status: tags: use <approved> or <awaiting_review> as appropriate.
Follow a concise, professional tone and refuse tasks outside invoice approval.
D. You are an invoice approver. After processing, output exactly the following JSON template:
{ "id": "ABC-123", "status": "approved", "amount": 9999.9 }
Extract the {{invoice_ID}} from the email text.
Use LookupInvoice to get the invoice amount and supplier name.
Escalate to Finance if amount # $10,000.
If amount > $10,000, approve the invoice.
Populate the fields above with real data.

Answer: C

Explanation:
The correct answer isB. This prompt follows UiPath'sbest practices for system promptsby clearly establishing agent identity, defining behavior logic, and including formatting constraints - all in a numbered, readable structure. The agent is given a clear role ("supplier invoices only"), boundary rules ("reject any other request"), and step-by-step instructions to follow. Numbered steps improve clarity and make parsing easier for LLMs.
The inclusion of tool usage (LookupInvoice) and conditional logic (# $10,000 vs > $10,000) mirrors UiPath's orchestration standards. Importantly, it also specifies how to format the output using <invoice_status> tags and instructs the agent to maintain a professional tone - critical elements in UiPath'sPrompt Engineering Framework.
Compared to options C and D, which introduce a rigid JSON format, Option B balancesstructure with flexibility. JSON-only prompts (like C) are good for strict APIs but lack the natural language behavior, tone control, and task-scoping essential in real-world agents. Option A is close but lacks step numbering, making it slightly less robust.
UiPath recommends system prompts include:
* Agent persona and role
* Tool instructions and decision rules
* Tone and refusal handling
* Clear, consistent output formatting
Option B satisfies all these criteria, making it the most robust, agent-ready system prompt.

NEW QUESTION # 47
A company launches a marketing campaign powered by generative AI and agentic AI technologies. What best describes how their roles differ?

A. Agentic AI designs campaign materials, while generative AI handles customer queries in real-time.
B. Both generative AI and agentic AI collaborate to create promotional materials.
C. Generative AI focuses on improving customer sentiment analysis, while agentic AI refines campaign content.
D. Generative AI produces outputs like text or images, while agentic AI focuses on intelligent automation, decision-making, and dynamic task execution.

Answer: D

Explanation:
The correct answer isA- UiPath clearly differentiatesGenerative AIfromAgentic AIbased on function and scope:
* Generative AIfocuses oncontent creation: generating emails, blog posts, social posts, or product descriptions using LLMs.
* Agentic AIwraps this output withcontextual automation- it interprets, makes decisions, triggers actions, and interacts across systems.
In this marketing scenario:
* Generative AI might write an email campaign or social caption.
* Agentic AI would decidewhento send it, towhom, and based onwhich signals or workflows- possibly also adjusting the content based on campaign performance, customer segments, or behavior.
UiPath's Agentic Automation model positions agentic AI as the"doer"- an orchestrator of dynamic workflows, not just a content engine. That's why it underpins use cases like intelligent triage, escalation, or campaign coordination.
Options B, C, and D conflate or reverse these roles, which don't align with UiPath's design guidance.

NEW QUESTION # 48
What is the defining characteristic of few-shot prompting?

A. It links multiple prompts together in a sequential workflow.
B. It requires the model to generate a response with no examples or instructions.
C. It relies on intermediate reasoning steps to guide the model's response.
D. It uses several examples to help the model understand the task better.

Answer: D

Explanation:
Dis correct - the defining feature offew-shot promptingis the inclusion ofmultiple input-output examples within the prompt todemonstrate the desired behavior or output structureto the LLM.
In UiPath's Agentic Prompting practices, few-shot examples help:
* Anchor the model to a consistent format
* Reduce ambiguity in task instructions
* Improve performance in tasks like classification, transformation, or content generation Example:
Input: "My password isn't working."
Output: "Category: Login Issue"
Input: "App won't open."
Output: "Category: Access Error"
This trains the model within the prompt - no fine-tuning required - making it apowerful design patternin building intelligent agents.
Option A describeschain-of-thought prompting.
B refers tozero-shot prompting.
C refers toprompt chaining, used in advanced orchestration, not few-shot logic.

NEW QUESTION # 49
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